A Probabilistic Generative Model for an Intermediate Constituency-Dependency Representation

We present a probabilistic model extension to the Tesnière Dependency Structure (TDS) framework formulated in (Sangati and Mazza, 2009). This representation incorporates aspects from both constituency and dependency theory. In addition, it makes use of junction structures to handle coordination constructions. We test our model on parsing the English Penn WSJ treebank using a re-ranking framework. This technique allows us to efficiently test our model without needing a specialized parser, and to use the standard evaluation metric on the original Phrase Structure version of the treebank. We obtain encouraging results: we achieve a small improvement over state-of-the-art results when re-ranking a small number of candidate structures, on all the evaluation metrics except for chunking

Exploiting Multiword Expressions to Solve “La Ghigliottina”

Il contributo descrive il sistema UNIOR4NLP, sviluppato per risolvere il gioco “La Ghigliottina”, che ha partecipato alla sfida NLP4FUN della campagna di valutazione Evalita 2018. Il sistema risulta il migliore della competizione e ha prestazioni più elevate rispetto agli umani.The paper describes UNIOR4NLP a system developed to solve “La Ghigliottina” game which took part in the NLP4FUN task of the Evalita 2018 evaluation campaign. The system is the best performing one in the competition and achieves better results than human players